Request for additional features and estimators in jamovi CFA

Discuss the jamovi platform, possible improvements, etc.

by Paul_Ginns » Sat Oct 26, 2019 1:08 am

Thanks very much for the jamovi confirmatory factor analysis module - this really makes CFA readily accessible.

Can I please suggest a few additional features?

1) it would be great if a test of multivariate normality could be reported; this would indicate whether the assumption of multivariate normality underpinning the maximum likelihood estimator is feasible. The MVN package might be used for such analyses - see http://www.biosoft.hacettepe.edu.tr/MVN/

2) following from the first point - would it be possible to support other estimators? It seems maximum likelihood or full information maximum likelihood where there are missing data are the only supported options, whereas lavaan supports the following (from https://rdrr.io/cran/lavaan/man/lavOptions.html ):

estimator: The estimator to be used. Can be one of the following: "ML" for maximum likelihood, "GLS" for generalized least squares, "WLS" for weighted least squares (sometimes called ADF estimation), "ULS" for unweighted least squares and "DWLS" for diagonally weighted least squares. These are the main options that affect the estimation. For convenience, the "ML" option can be extended as "MLM", "MLMV", "MLMVS", "MLF", and "MLR". The estimation will still be plain "ML", but now with robust standard errors and a robust (scaled) test statistic. For "MLM", "MLMV", "MLMVS", classic robust standard errors are used (se="robust.sem"); for "MLF", standard errors are based on first-order derivatives (information = "first.order"); for "MLR", ‘Huber-White’ robust standard errors are used (se="robust.huber.white"). In addition, "MLM" will compute a Satorra-Bentler scaled (mean adjusted) test statistic (test="satorra.bentler") , "MLMVS" will compute a mean and variance adjusted test statistic (Satterthwaite style) (test="mean.var.adjusted"), "MLMV" will compute a mean and variance adjusted test statistic (scaled and shifted) (test="scaled.shifted"), and "MLR" will compute a test statistic which is asymptotically equivalent to the Yuan-Bentler T2-star test statistic (test="yuan.bentler.mplus"). Analogously, the estimators "WLSM" and "WLSMV" imply the "DWLS" estimator (not the "WLS" estimator) with robust standard errors and a mean or mean and variance adjusted test statistic. Estimators "ULSM" and "ULSMV" imply the "ULS" estimator with robust standard errors and a mean or mean and variance adjusted test statistic.
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